The present invention relates to an adaptive input-output apparatus and in particular, relates to an adaptive input-output apparatus which is used in recognition, prediction and motion control of robots and the like. More specifically, the present invention relates to an adaptive input-output apparatus. The adaptive input-output apparatus uses the Bayes method for minimizing the mean of functions for a probability distribution in parameter space, and the K approximation method for determining the closest element of the three-dimensional elements of the given elements, and neural network modeling which obtain optimum values by learning, future mapping and other methods when a problem to be solved is given.
Recently, it has become necessary to have apparatus and equipment provided with a flexible application performance across various fields of engineering. Thus, an adaptive input-output apparatus fills an important role as a technical element of such apparatus.
Prior art FIG. 1 shows the constitution of a conventional adaptive input-output apparatus. The adaptive input-output apparatus 211 shown in the figure is comprised of the input/output portion 214 for the determination of the input/output function 213 which is determined from the sample data 216, and a data management portion 215 which holds and manages the sample data 216.
The sample data 216 of the data management portion 215 is expressed as pairs of input and output values (x.sub.i, y.sub.i) (where i=1,2, . . . n) in accordance with the truth function which describes a desirable input-output relationship. The input/output portion 215 refers to the sample data (x.sub.i, y.sub.i) and determines the input-output function f.sub.N (x).
For example, when a neural network is used in the input in the input/output portion 214, the sample data 216 is used as learning data. A input/output portion 214 which uses a neural network learns so that the output error e.sub.1 is minimized by the learning and approximates the truth function. Accordingly, the input/output portion supplements the input 212 which does not exist in the sample data 216. Also, it is possible for the output 218 of the adaptive input-output apparatus 211 to provide prompt responses with respect to the input 212.
Prior art FIG. 2 shows details of a conventional adaptive input-output apparatus.
The data management portion 215 has a data holding portion 219 for storing the sample data 216, and when this example is realized by a neural network, the sample data 216 (P.sub.i =x.sub.i, y.sub.i) for learning is taken from the data holding portion 219 and applied at a predetermined timing to the input/output portion 214 in FIG. 1.
The input/output portion 214 uses the sample data 216 given from the data holding portion 219, and determines the input/output function 213 (f.sub.N (x)) so that the truth function f.sub.true (x.sub.i) is approximated.
FIG. 2A shows the case when there is a small quantity of sample data 216 in the data holding portion 219. In this case, when there is a small quantity of sample data 216, the accuracy of the output 218 deteriorates since the input/output function 213 does not sufficiently approximate the truth function "b". FIG. 2B shows the case when there is a large quantity of sample data 216 in the data holding portion 219. As shown in FIG. 2A, the truth function is not approximated when there is a small quantity of sample data 216 of the data holding portion 219, but as shown in FIG. 2B, there is interpolation of the sample data which does not exist in FIG. 2A when there is an increase in the quantity of sample data 216 so that the input/output function 213 sufficiently approximates the truth function "b" and the accuracy of the output 218 improves as a result.
However, as shown in FIG. 2A, a conventional adaptive input-output apparatus has a rough input/output function 213 when there is a small quantity of sample data 216 and fine sample data are not determined, hence making it difficult to obtain a input/output function 213 of a shape which approaches the truth function "b". As shown in FIG. 2B, when there is an increase in the quantity of sample data 216, there is the supply of sample data 216 which includes fine values and it is possible to obtain a input/output function 213 which is in agreement with the truth function "b". However, when learning using many sample data 216 is performed, there are the problems of an increase in the amount of processing and a resultant increase in the accompanying processing cost.